Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Genomics

Open Access

MicroRNA profiling of rhesus macaque embryonic stem cells

Contributed equally
BMC Genomics201112:276

https://doi.org/10.1186/1471-2164-12-276

Received: 30 December 2010

Accepted: 31 May 2011

Published: 31 May 2011

Abstract

Background

MicroRNAs (miRNAs) play important roles in embryonic stem cell (ESC) self-renewal and pluripotency. Numerous studies have revealed human and mouse ESC miRNA profiles. As a model for human-related study, the rhesus macaque is ideal for delineating the regulatory mechanisms of miRNAs in ESCs. However, studies on rhesus macaque (r)ESCs are lacking due to limited rESC availability and a need for systematic analyses of fundamental rESC characteristics.

Results

We established three rESC lines and profiled microRNA using Solexa sequencing resulting in 304 known and 66 novel miRNAs. MiRNA profiles were highly conserved between rESC lines and predicted target genes were significantly enriched in differentiation pathways. Further analysis of the miRNA-target network indicated that gene expression regulated by miRNAs was negatively correlated to their evolutionary rate in rESCs. Moreover, a cross-species comparison revealed an overall conservation of miRNA expression patterns between human, mouse and rhesus macaque ESCs. However, we identified three miRNA clusters (miR-467, the miRNA cluster in the imprinted Dlk1-Dio3 region and C19MC) that showed clear interspecies differences.

Conclusions

rESCs share a unique miRNA set that may play critical roles in self-renewal and pluripotency. MiRNA expression patterns are generally conserved between species. However, species and/or lineage specific miRNA regulation changed during evolution.

Background

ESCs are derived from the inner cell mass (ICM) of blastocyst-stage embryos [1, 2]. Self-renewal and pluripotency enable ESCs to be a renewable and versatile model to study developmental biology. Moreover, ECSs have potential applications in regenerative medicine. Rhesus macaques (Macaca mulatta) are a well-studied primates, and with genetic and physiological similarities to humans, rhesus macaques have become an ideal model for ESC-based therapies [3]. However, the study and application of rESCs are lacking compared with those of mouse and human ESCs due to limited rESC line availability and a need for systematic analyses of fundamental rESC characteristics.

MiRNAs are small endogenous non-coding transcripts (~19-25 nt) with diverse roles in development, differentiation and oncogenesis. MiRNAs bind to complementary sites within cognate mRNA 3' UTRs, resulting in degradation, deadenylation or translational repression, which provide a crucial level of post-transcriptional regulation [4]. Moreover, tissue- and cell type-specific miRNA expression patterns have been described [59], which elucidate various miRNA functions in specific conditions. MiRNAs also play important roles in ESCs as demonstrated by deletion of Dicer or DGCR8 in mouse ESCs resulting in proliferation and differentiation defects [1012]. Previous studies of miRNA expression patterns in mouse and human ESCs have revealed a unique miRNA set that is distinct from other cell types and tissues [6, 13, 14]. Several miRNAs preferentially expressed in human and mouse ESCs, and down-regulated in differentiated cells are key regulators of 'stemness' [1519]. However, the miRNA expression profile of rESCs is unknown.

ESC lines derived from the same species may contain distinct miRNA profiles and share only a small number of miRNAs [20]. This observation is likely caused by various ESC culture conditions rather than inherent genetic variation within embryos used for ESC derivation [21, 22]. Another factor is the use of numerous analyses for detecting miRNA patterns due to the limited resolution of techniques such as microarray analysis [23]. However, recent advancements in next-generation sequencing technology provide an ideal tool for analyzing the miRNA transcriptome with high resolution to identify novel miRNAs [24].

In this study, we isolated and characterized three rESC lines, and performed miRNA profiling using Solexa sequencing. Our miRNA study of rESCs and cross-species comparison may assist future studies for understanding and modulating ESC regulatory networks.

Results

Isolation and characterization of rESC lines

Thirteen expanded rhesus macaque blastocysts with a prominent ICM were selected by immunosurgery and 11 ICMs were isolated and plated onto feeder cells. ICMs attached to feeder cells within 48 h and three ESC-like ICM outgrowths appeared after 7-8 days. ICM outgrowths were manually dissociated into 4-6 smaller clumps using a microscalpel, excised from feeder cells and replated onto fresh mouse embryonic fibroblasts (mEFs). Clones with distinct boundaries and high nuclear to cytoplasm ratios were selected for further propagation. Three rESC lines were established and designated as IVF1.2, IVF3.2 and IVF3.3. IVF3.2 and IVF3.3 were derived using the same sperm and oocyte donors. rESCs shared common morphologies with other primate ESCs such as being flat with a distinct boundary against feeder cells. Cells showed high nuclear to cytoplasm ratios and prominent nucleoli (Figure 1A). IVF1.2 and IVF3.3 were cultured for >60 passages and IVF3.2 for >80. Pluripotency markers were highly expressed in all rESC lines including Oct-4, Nanog, SSEA-4, TRA-1-60 and TRA-1-81 (Figure 1A). Other crucial transcription factors, such as Sox-2 and Rex-1, were also detected by RT-PCR (data not shown).
Figure 1

'Stemness' and pluripotency of rESC lines. (A): (a-g): IVF1.2; (h-n): IVF3.2; (o-u): IVF3.3; (a, h, o): Phase-contrast micrograph of an ESC colony on mEFs; (b, i, p): Alkaline phosphatase staining; (c, j, q): Oct-4; (d, k, r): Nanog; (e, l, s): SSEA-4; (f, m, t): TRA-1-60; (g, n, u): TRA-1-81; (B): (a-f): IVF1.2; (g-l): IVF3.2; (m-r): IVF3.3; (a, g, m): Neuronal marker Tuj III expression (green); (b, h) Cardiomyocyte marker α-cardiac actinin (green); (n): Cardiomyocyte marker Troponin C (green); (c, i, o) Endoderm marker α-fetoprotein (AFP, green); (d, j): Neural tube; (p): Squamous epithelium; (e): Cartilage; (k): Muscles; (q): Adipose; (f, l): Intestinal epithelia; (r): Gland. Scale bars: (A: a-u, 100 μm; B: a, g, m, c, i, o, 100 μm; b, h, n, 20 μm; d-f, j-l, p-r, 50 μm).

All rESC lines showed strong alkaline phosphatase activity (Figure 1A) and could spontaneously differentiate into cell lineages from the three embryonic germ layers. After 2 days in suspension culture, rESC clones formed embryoid bodies (EBs). After 5 days, cavities were observed in EBs, which are termed cystic EBs. EBs attached to gelatin-coated dishes and were cultured for 5-7 days until various differentiated cells appeared including neuron-like cells, contractive cardiomyocytes and endoderm-like cells (Figure 1B). Teratomas were formed 6-7 weeks after rESC injection into the hind leg muscles of SCID mice. Histological characterization revealed that ectoderm (neural tube and squamous epithelium), mesoderm (cartilage, muscle and adipose) and endoderm (intestinal epithelia and glands) like structures were present in teratomas (Figure 1B). These results demonstrated that rESC lines could differentiate into all three embryonic germ layers in vitro and in vivo.

Detailed G-banding analysis revealed that all rESC lines were karyotypically normal with a diploid set of 42 chromosomes (Fig. S1 in Additional file 1), even after long-term culture in vitro and repeated freeze-thaw procedures. IVF1.2 and IVF3.3 were derived from male blastocysts (40+XY) and IVF3.2 was derived from a female blastocyst (40+XX).

Small RNA Sequencing

Newly established rESCs were tested for purity using Oct-4 staining, which resulted in >95% Oct-4-positive cells (Fig. S2 in Additional file 1). A small RNA library from each cell line was then constructed for sequencing. Small RNA library sequencing yielded 12.66 × 106, 13.12 × 106and 11.57 × 106 raw reads from IVF1.2, IVF3.2 and IVF3.3, respectively. After filtering, 10.89 × 106 (IVF1.2), 10.60 × 106 (IVF3.2) and 9.26 × 106 (IVF3.3) clean reads (18-30 nt) were obtained. The length distribution of clean reads centered at 22-23 nt, coinciding with the miRNA length range. Sequence alignment with the rhesus macaque genome (rheMac2) using the Short oligonucleotide alignment program (SOAP) [25] demonstrated that 8.36 × 106 (IVF1.2), 7.68 × 106 (IVF3.2) and 7.15 × 106 (IVF3.3) sequences were mapped to the genome (Figure 2A). MiRNAs were the major component of small RNA libraries from rESCs (Figure 2B) being 60% of sequences, which were matched to reference miRNAs in each sample. The proportions of other annotated RNAs was <15%. Notably, ~25% of sequences could not be annotated because of low coverage (6 × ) and relatively poor annotations of the available rhesus macaque genome. Unknown sequences were further analyzed to identify novel miRNAs.
Figure 2

Summary of small RNA sequencing. (A): Counts and proportion of mapped tags in total clean reads; (B): Annotation of 13 RNA categories in pie chart for IVF1.2, IVF3.2 and IVF3.3.

MiRNA expression profiles of rESC lines

After matching to 466 reference rhesus macaque miRNAs, 326, 329 and 326 known miRNAs were detected in IVF1.2, IVF3.2 and IVF3.3, respectively, with absolute counts ranging from 1-1,702,656 (Table S1 in Additional file 1). Among them, 304 miRNAs were shared by the three pools and remaining miRNAs were expressed at low levels with <10 counts. From a genome-wide perspective, very low miRNAs were likely sequencing artifacts or miRNA machinery byproducts [26]. Therefore, an absolute count of 30 was set for each pool as the minimum threshold for further analyses.

To compare miRNA expression patterns between rESC lines, we evaluated the expression variability (Var) and coefficient of variation (C.V.) of miRNA clusters (Figure 3A, 3B). The results revealed similar expression patterns of miRNA clusters in rESC lines. We also compared the expression of total miRNAs using a differential index (D.I.) and Kappa Statistical analyses. For the D.I., most miRNAs were consistently expressed (Figure 3C), whereas a slight deviation existed in IVF1.2 due to low-level expressed miRNAs. For Kappa Statistical analysis, there was high repeatability between any two cell lines (Figure 3D). Taken together, miRNA expression patterns in rESC lines were highly consistent.
Figure 3

Consistency of miRNA expression among three rESC lines. Pairwise comparisons of miRNA clusters between IVF1.2, IVF3.2 and IVF3.3 were performed. Each triangle dot represents an individual pairwise comparison of a miRNA cluster based on variability (A) and CV statistics (B); (C): Trio-comparison of distinct miRNA expression between IVF1.2, IVF3.2 and IVF3.3 based on the differential index. Each point indicates the normalized count of each miRNA; (D): Association analysis of total miRNA expression between IVF1.2, IVF3.2 and IVF3.3 using Kappa statistical analysis and Z-score.

Identification of novel miRNAs

We predicted novel miRNA genes in unannotated sequences using Mireap (http://sourceforge.net/projects/mireap/). Briefly, according to the characteristic hairpin structure of miRNA precursors, unannotated small RNA sequences mapped to genome were downloaded with flanking sequences and analyzed for secondary structure, Dicer cleavage sites and minimum free energy (mfe). After analysis, 141,635 reads that contained 135 novel miRNA precursors with >30 reads were identified. There were 62 novel miRNA precursors (66 mature miRNAs) shared between the three rESC lines (Table S2 in Additional file 1) and 19 (23 mature miRNAs) were homologous to reference miRNAs (miRBase V16.0) in other species (Table 1). Of the 19 miRNA precursors with homologs, 16 precursors were only conserved in humans and other primates, but not rodents.
Table 1

Novel miRNAs identified in rESCs

Mature miRNA sequence

MiRNA precursor location

Name

Reads

   

IVF1.2

IVF3.2

IVF3.3

TATGGAGGTCTCTGTCTGGCT

chr1:205,580,537-205,580,616:-

miR-1843-5p

124

159

152

TCTGATCGTTCCCCTCCATACA

chr1:205,580,537-205,580,616:-

miR-1843-3p

127

174

193

TGATGGGTGAATTTGTAGAAGG

chr1:70,976,129-70,976,208:-

miR-1262-3p

2,557

2,267

2,150

GTTGGGACAAGAGAACGGTCTT

chr1:158,332,926-158,333,000:-

miR-3122-5p

252

371

214

AATTCCCTTATGGATAATCTGG

chr2:80519804-80519882:+

miR-3938-3p

138

227

104

CATGCTAGAACAGAAAGAATGGG

chr3:106,443,245-106,443,327:+

miR-3146-3p

37

120

61

TATTTTGAGTGTTTGGAATTGA

chr4:125,407,861-125,407,939:+

miR-3145-3p

35

36

31

GAAAATGATGAGTAGTGACTGATG

chr4:128869861-128869951:+

miR-3622-3p

72

243

73

AAGAGCTTTTGGGAATTCAGGTAG

chr5:144,708,317-144,708,405:-

miR-3140-3p

151

358

200

ATATACAGGGGGAGACTCTTAT

chr7:164,327,476-164,327,555:+

chr7:164,328,696-164,328,775:+

miR-1185-3p

52

176

105

CGGGAACGTCGAGACTGGAGC

chr7:164,844,819-164,844,896:-

miR-1247-3p

127

131

32

TTAGGGCCCTGGCTCCATCTCC

chr9:73,887,081-73,887,164:+

miR-1296-5p

33

123

53

TCGACCGGACCTCGACCGGCTCG

chr9:103,084,702-103,084,783:-

miR-1307-5p

291

872

944

ACTCGGCGTGGCGTCGGTCGTGG

chr9:103,084,702-103,084,783:-

miR-1307-3p

1,936

3,975

3,030

GACTCTAGCTGCCAAAGGCGCT

chr11:98,713,141-98,713,223:+

miR-1251-5p

40

97

59

TGTGGGACCTCTGGCCTTGGC

chr11:105706113-105706195:+

miR-3922-3p

192

250

248

TGCGGGGCTAGGGCTAACAGCA

chr16:11,829,804-11,829,901:+

miR-744-5p

11,447

24,797

14,171

CTGTTGCCACTAACCTCAACC

chr16:11,829,804-11,829,901:+

miR-744-3p

75

81

104

TTTCCGGCTCGCGTGGGTGTGT

chr16:18,891,345-18,891,423:-

miR-1180-3p

30

133

77

CCGTCCTAAGGTTGTTGAGTT

chrX:68,990,098-68,990,174:+

miR-676-3p

46

206

206

TTCATTCGGCTGTCCAGATGTA

chrX:113,236,410-113,236,505:+

miR-1298-5p

246

893

852

CATCTGGGCAACTGACTGAACT

chrX:113,236,410-113,236,505:+

miR-1298-3p

30

98

58

TGAGTACCGCCATGTCTGTTGGG

chrX:113,271,154-113,271,232:+

miR-1911-5p

88

843

473

MiRNA target gene prediction and functional classification

Three programs were used to predict miRNA target genes. Using the TargetScan program, a custom set of Perl codes were downloaded from the TargetScan database (Release 5.1, http://www.targetscan.org/). Conserved and non-conserved predicted patterns of miRNA targets were considered separately. For PITA and miRanda, we used 238 expressed miRNAs (number of reads ≥30) and rhesus macaque 3'UTRs orthologous to annotated human 3'UTRs as inputs to predict miRNA targets. Over-representation of predicted miRNA targets for both conserved and non-conserved patterns based on the TargetScan program in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway is shown in Table S3 in Additional file 1 (P≤0.05, after Benjamini Hochberg correction). Gene enrichment patterns obtained by various methods were similar to each other, indicating that miRNAs correlated to functionally critical roles in rESCs. For example, predicted targets involved in the MAPK (hsa04010), Wnt (hsa04310) and TGF-beta signaling pathways (hsa04350), and pathways involved in cancer (hsa05200) were significantly enriched (Table S3 in Additional file 1).

MiRNA-target interaction network analysis

A systematic survey of miRNAs that interacted with targets may provide a better understanding of miRNA regulation in stem cells. Thus, we analyzed potential interactions between miRNAs and their targets, which is termed as the miRNA-target network (MT network). All miRNAs and their targets were used to generate a bipartite graph of miRNA-target interactions.. Nodes represent miRNAs or target genes and edges correspond to interactions between miRNAs with target genes. Node degree in the MT network is the number of connections or edges with other nodes. For each target gene node, degrees represent the number of miRNAs targeting the gene. Globally, the MT network was comprised of 8,934 nodes with 48,546 edges. Using MT network degree analysis (see methods), the degree of genes regulated by miRNAs was negatively correlated (r = -0.564 with p = 0.018 based on Pearson's correlation) with their evolutionary rate (dN/dS) (Figure 4A). Similarly, a previous study reported a negative correlation between the degree and evolutionary rate of proteins in a protein-protein interaction network [27].
Figure 4

The degree of genes regulated by miRNAs is negatively correlated with their evolutionary rate in rESCs. (A): The correlation coefficient (r = -0.564 with P = 0.018 based on Pearson's correlation) between dN/dS ratios and degrees of genes regulated by miRNAs in rESCs; (B): The correlation coefficient (r = -0.556 with P = 0.039 based on Pearson's correlation) between dN/dS ratios and degrees with normalization of 3'UTR length per 1 kb; (C): The correlation coefficient (r = -0.533 with P = 0.019 based on Pearson's correlation) between dN/dS ratios and degrees with normalization of 3'UTR length per 2 kb; (D): The degree of miRNAs and their targets in the random network is significantly lower (p < 2.2e-16 with a Wilcoxon rank sum test) compared with those in the MT network.

To exclude factors with potential effects on the observed negative correlation, we tested the effect of 3'UTR length. After length normalization, the negative correlation remained (Figure 4B,4C). Another factor was a potential ascertainment bias caused by miRNA target prediction and identification of orthologous macaque 3'UTRs. To evaluate this possibly, we randomized the scale-free MT network based on Barabasi-Albert model. The degree of miRNA and target genes in the random network was significantly lower (p < 2.2e-16 with a Wilcoxon rank sum test) compared with that observed in the MT network (Figure 4D). A significant correlation was not observed in the random network, which further supported the negative correlation in the MT network.

Comparison of miRNA profiles from mouse, human and rhesus macaque

To compare ESC miRNA profiles between species, we retrieved previously reported mouse and human ESC miRNA profiles. To avoid a cross-talking bias due to various sequencing techniques and data sizes, we selected Solexa sequencing datasets from mouse and human ESCs [14, 28]. MiRNA expression frequencies were normalized to each other by determining the expected frequency in mapped reads per million. MiRNA expression levels were calculated as the mean of three samples. The three species expressed 138 common miRNAs. Clustering analysis based on the 138 shared miRNAs revealed that miRNA expression profiles were more similar between human and rhesus macaque (Fig. S3 in Additional file 1), which was consistent with their evolutionary relationship. A number of miRNAs were stably and highly expressed between species, such as the miR-17 family, miR-21 and miR-103, which suggested conserved functions in ESCs during evolution.

Of the 138 common miRNAs the 'stemness' miR-290-295 cluster [13, 29] was the highest expressed in mouse ESCs. However, primate homologs the miR-290-295 and miR-371-373 clusters were not highly expressed in human and rESCs. Instead, the miR-302 cluster was the highest expressed in human and rhesus macaque [14, 30, 31], indicating the functional divergence of stemness miRNA clusters in primate lineages. The miR-290-295 cluster contains multiple mature miRNAs with seed sequences similar or identical to those in the miR-302 cluster [6]. MiR-290-295 and miR-302 clusters are transcriptionally regulated by Oct-4/Sox2 [29]. Taken together, these observations suggest that these two clusters are similarly expressed, regulate common target genes and are functionally conserved in ESCs derived from various species.

There are three miRNA clusters differentially expressed in mouse, human and rESCs. The chromosome 19 miRNA cluster (C19MC), a primate-specific miRNA cluster [32], was enriched in human ESCs, but almost absent in rESCs (Figure 5C). A conserved miRNA cluster within the imprinted Dlk1-Dio3 region was highly expressed in mouse and rhesus macaque ESCs, but rarely expressed in human ESCs (Figure 5B), which was consistent with results from four other human ESC lines [31].The miR-467 cluster in the sfmbt2 gene intron region was only expressed in mouse ESCs (Figure 5A), embryos and newborn ovaries [33, 34], but not in human and rhesus macaque ESCs, consistent with previous reports [29].
Figure 5

Expression differences of three miRNA clusters among human, rhesus macaque and mouse ESCs. (A): MiR-467cluster expression in mouse (mmu) ESCs; (B): Expression of the miRNA cluster in the imprinted Dlk1-Dio3 region in mouse, rhesus macaque (mml) and human (hsa) ESCs; (C): C19MC expression in rhesus macaque (mml) and human (hsa) ESCs. Reads were normalized to the mapped reads per million.

Discussion

We established three rESC lines (IVF1.2, IVF3.2 and IVF3.3) and profiled miRNA expression using Solexa sequencing. MiRNA expression profiles were generally conserved between the three rESC lines, sharing 92.4% of expressed miRNAs. IVF3.2 and IVF3.3 were more similar because of derivation from identical sperm and ovum donors. Similarly, Navara et al. [35] reported that pedigreed rESCs express homogeneous gene profiles. Compared with known miRNAs in our data set, most novel miRNAs were moderately expressed. Novel miRNA biological functions and association with stemness are yet to be elucidated.

We predicted miRNA targets in rESCs using three independent algorithms. Targets involved in TGF-β, MAPK and Wnt signaling pathways were significantly enriched. These pathways play important roles in mouse and human ESC differentiation [3638]. This observation suggests that in addition to transcriptional level regulation by stemness-associated factors such as Oct4 and Nanog, miRNAs may also play an important role in maintaining ESC self-renewal and pluripotency at the post-transcriptional level. Moreover, over-represented miRNA targets involved in cancer pathways support the observation that cancer and ESCs share pathways for self-renewal and proliferation [38].

Our systematic investigation of the MT network suggested that the degree of genes regulated by miRNAs was negatively correlated with their evolutionary rate in rESCs. Genes with more miRNA regulators evolve more slowly. This was anticipated in accordance to a rule of "the more conserved, the deeper regulated" has been proposed in protein-protein interaction networks, in which hubs or central proteins are functionally essential and usually lethal after gene knockout [27]. Therefore in the MT network, hub targets should be more regulated due to selective pressure, which indicates the fine regulation of miRNAs in ESCs.

The cross-species comparison indicated that the majority of shared miRNAs were stably expressed between species and consistent with miRNA sequence-level conservation. This observation suggested that miRNA expression is under stabilizing selection due to functional constraints on miRNAs in ESCs. Despite this close relationship, miRNA expression differences existed between human and rhesus macaque ESCs. The chromosome 19 miRNA cluster (C19MC) is a primate-specific miRNA cluster and contains >30 mature miRNAs. These clustered miRNAs are expressed in human ESCs, placenta and fetal brain [14, 31, 32, 39], suggesting that they are involved in embryogenesis. However, the C19MC cluster is almost absent in rESCs. This expression difference suggests that even between closely related species, there may be a significant miRNA expression divergence and regulatory differences during embryogenesis between rhesus macaques and humans. In addition, the miRNA cluster in the imprinted Dlk1-Dio3 region was conserved at the sequence level between mice, rhesus macaques and humans. Liu et al reported that the expression of this cluster is positively correlated to mouse stem cell pluripotencies (ESCs vs iPS cells) [40]. We found that this cluster was enriched in mouse and rhesus macaque ESCs, but rare in human ESCs. Recently, it was reported that diverse temporal origins may be responsible for observed differences between mouse and human ESCs [41]. Our data suggests that miRNA expression patterns may provide a new strategy for investigating interspecies differences in ESC pluripotency.

Conclusions

Our results indicate that miRNA profiles are highly conserved and consistent between the three rESC lines, and these miRNAs may play critical roles in the maintenance of self-renewal and pluripotency in rESCs, particularly in differentiation pathways. Further analysis of the miRNA-target network indicates that the degree of genes regulated by miRNAs is negatively correlated with their evolutionary rate in rESCs. This observation suggests important roles of miRNAs in post-transcriptional regulation. Moreover, cross-species comparison of ESCs revealed an overall conservation of miRNA expression patterns, indicating stabilizing selection on miRNA expression in ESCs during evolution. However, we identified three miRNA clusters (miR-467, the miRNA cluster in the imprinted Dlk1-Dio3 region and C19MC) that show distinct differences between mouse, human and rhesus macaque ESCs, which suggests species and/or lineage specific miRNA regulatory changes during evolution.

Methods

rESC isolation and characterization

rESCs were derived from the ICM of rhesus macaque blastocyst-stage embryos and characterized using standard protocols. rESC isolation and characterization details are provided in the supplemental Methods in Additional file 1.

RNA extraction and small RNA sequencing

Total RNA was extracted using a mirVana miRNA isolation kit (Ambion). According to the manufacturer's instructions (Illumina), the small RNA fraction between 18-30 nt was isolated from total RNA by PAGE (polyarclymide gel electrophoresis) purification and ligated to a pair of adaptors at the 5' and 3' ends. Small RNA molecules were converted to cDNA and amplified by RT-PCR using adaptor primers. Unique 6 bp indexed sequences were added to 5' adapters for multiplex sequencing. Purified DNA was directly used for cluster generation and sequencing analysis using an Illumina Genome Analyzer. These data have been deposited in the GEO database (GSE27886).

Small RNA annotation and novel miRNA prediction

For clean reads, the original 35 nt reads from sequencing were filtered by trimming the 5' and 3' adaptors, eliminating contaminants, and inadequate (<18 nt) and low quality reads. For detecting known miRNAs, clean reads were matched to reference miRNA precursor sequences in the rhesus macaque (miRBase version 16.0) using Blastall (-p blastn -F F -e 0.01). MiRNA absolute expression was the sum of clean reads mapped to the precursor. For annotating degradation tags of rRNA, tRNA,small cytoplasmic RNA (scRNA), small nuclear RNA (snRNA) and small nucleolar RNA (snoRNA), clean reads were aligned with Genbank and Rfam 9.1 databases using Blastall. For identifying repeat associated RNA and degradation tags of mRNA, clean reads were overlapped with repeat sequences, exons and introns of mRNAs. In the initial alignment and annotation, some small RNA tags were mapped to more than one category. For uniquely mapped reads, the following priority rules were applied: rRNA/tRNA/scRNA/snRNA/snoRNA (Genbank > Rfam) > known miRNA > repeat > exon > intron. Unannotated small RNA tags were termed as 'unann'. Mireap (http://sourceforge.net/projects/mireap/) was used to predict novel miRNAs.

MiRNA expression profiling analysis

Based on the randomly selected 10,000 bins for each chromosome with each bin spanning a 50 bp window (210,000 bins in 21 chromosomes), miRNAs with sporadic reads were filtered out and miRNAs with >30 reads (p < 0.0001 based on Poisson distribution simulation) were treated as true reads for further analyses. miRNA expression profiles were compared and evaluated in rECSs at cluster and individual miRNA levels. A miRNA cluster was defined as consecutive miRNAs with a ≤10 kb inter-miRNA distance (MID). For each level, two independent methods were used for confirmation. Var and C.V. were used to survey miRNA member divergences in a cluster. Var was used to represent a divergence measure based on Shannon entropy [42]. D.I. and Kappa Statistical analysis (κ) were used to compare total expressed miRNAs in rESCs. D.I. was used to evaluate relative deviation of individual miRNA expression levels in rESCs. Kappa statistical analysis was used to measure the repeatability of total miRNAs expression between any two samples (supplemental methods in Additional file 1).

MiRNA target prediction

To predict miRNA target genes, TargetScanS [43], miRanda (http://www.microrna.org/microrna/home.do) and PITA (http://genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html) were used. Common miRNAs (≥30 reads) were used to predict target genes. For each miRNA, the reference mature miRNA sequence in miRBase was used. For 3'UTR identification in the rhesus macaque, human annotated 3'UTRs from Refseq and orthologous 3'UTRs from six species (human, chimpanzee, macaque, mouse, rat and dog) were extracted from multi17way aligned files downloaded from the UCSC database [44]. Orthologous 3'UTRs were aligned using the MUSCLE [45] program to obtain multi6way 3'UTR files required for analysis.

Gene ontology (GO) analyses of predicted miRNA targets were performed using t DAVID Bioinformatics Resources [46] (http://david.abcc.ncifcrf.gov/home.jsp). Ascertainment biases such as a relatively low number of annotated miRNAs, 3'UTRs in the rhesus macaque, ≥30 cutoffs for miRNA reads and false predicted miRNA targets were assessed by simulating the bias with 240 randomly synthesized in silico 22 nt miRNAs (equal number with true expression).

Potential correlations between miRNA (or target) degrees connected in the MT network, miRNA expression and target evolution were evaluated (supplemental methods in Additional file 1).

Notes

Abbreviations

ESCs: 

embryonic stem cells

ICM: 

inner cell mass

EB: 

embryoid body

miRNA: 

microRNA

nt: 

nucleotide

scRNA: 

small cytoplasmic RNA

snRNA: 

small nuclear RNA

snoRNA: 

small nucleolar RNA

Var: 

variability

C.V.: 

coefficient of variation

D.I.: 

differential index

κ

Kappa Statistic

MT network: 

miRNA-target regulatory network

iPS cell: 

induced pluripotent stem cell

C19MC: 

chromosome 19 miRNA cluster

mfe: 

minimum free energy

SOAP: 

Short oligonucleotide alignment program

SCID: 

severe combined immune deficiency

mEFs: 

mouse embryonic fibroblasts

dN/dS: 

nonsynonymous vs. synonymous substitution rate.

Declarations

Acknowledgements

We thank Hui Zhang, Yuyu Niu, Bin Lu, Tao Tan and Xiangyu Guo for technical assistance. This study was supported by grants from the 973 program of China (2011CBA01101), the National 863 project of China (2006AA02A116 and 2006AA02A101), the Major Project of Ministry of Science and Technology (2009ZX09501-028), the Chinese Academy of Sciences (KSCX1-YW-R-34, Westlight Doctoral Program) and the National Natural Science Foundation of China (30871343).

Authors’ Affiliations

(1)
State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences
(2)
Department of Reproduction and Development, Kunming Institute of Zoology, Chinese Academy of Sciences
(3)
Kunming Biomed International and National Engineering Research Center of Biomedicine and Animal Science
(4)
Graduate School of Chinese Academy of Sciences

References

  1. Thomson J, Kalishman J, Golos T, Durning M, Harris C, Becker R, Hearn J: Isolation of a primate embryonic stem cell line. Proc Natl Acad Sci USA. 1995, 92: 7844-10.1073/pnas.92.17.7844.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Thomson J, Itskovitz-Eldor J, Shapiro S, Waknitz M, Swiergiel J, Marshall V, Jones J: Embryonic stem cell lines derived from human blastocysts. Science. 1998, 282: 1145-View ArticlePubMedGoogle Scholar
  3. Wolf D: Nonhuman primate embryonic stem cells: an underutilized resource. Regenerative Medicine. 2008, 3: 129-131. 10.2217/17460751.3.2.129.View ArticlePubMedGoogle Scholar
  4. He L, Hannon G: MicroRNAs: small RNAs with a big role in gene regulation. Nature Reviews Genetics. 2004, 5: 522-531. 10.1038/nrg1379.View ArticlePubMedGoogle Scholar
  5. Sempere LF, Freemantle S, Pitha-Rowe I, Moss E, Dmitrovsky E, Ambros V: Expression profiling of mammalian microRNAs uncovers a subset of brain-expressed microRNAs with possible roles in murine and human neuronal differentiation. Genome Biol. 2004, 5: R13-10.1186/gb-2004-5-3-r13.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Suh MR, Lee Y, Kim JY, Kim SK, Moon SH, Lee JY, Cha KY, Chung HM, Yoon HS, Moon SY, et al: Human embryonic stem cells express a unique set of microRNAs. Dev Biol. 2004, 270: 488-498. 10.1016/j.ydbio.2004.02.019.View ArticlePubMedGoogle Scholar
  7. Lee E, Gusev Y, Jiang J, Nuovo G, Lerner M, Frankel W, Morgan D, Postier R, Brackett D, Schmittgen T: Expression profiling identifies microRNA signature in pancreatic cancer. International Journal of Cancer. 2007, 120: 1046-1054.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Volinia S, Calin G, Liu C, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M: A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA. 2006, 103: 2257-10.1073/pnas.0510565103.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Calin G, Croce C: MicroRNA signatures in human cancers. Nature Reviews Cancer. 2006, 6: 857-866. 10.1038/nrc1997.View ArticlePubMedGoogle Scholar
  10. Murchison EP, Partridge JF, Tam OH, Cheloufi S, Hannon GJ: Characterization of Dicer-deficient murine embryonic stem cells. Proc Natl Acad Sci USA. 2005, 102: 12135-12140. 10.1073/pnas.0505479102.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Kanellopoulou C, Muljo SA, Kung AL, Ganesan S, Drapkin R, Jenuwein T, Livingston DM, Rajewsky K: Dicer-deficient mouse embryonic stem cells are defective in differentiation and centromeric silencing. Genes Dev. 2005, 19: 489-501. 10.1101/gad.1248505.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Wang Y, Medvid R, Melton C, Jaenisch R, Blelloch R: DGCR8 is essential for microRNA biogenesis and silencing of embryonic stem cell self-renewal. Nat Genet. 2007, 39: 380-385. 10.1038/ng1969.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Houbaviy HB, Murray MF, Sharp PA: Embryonic stem cell-specific MicroRNAs. Dev Cell. 2003, 5: 351-358. 10.1016/S1534-5807(03)00227-2.View ArticlePubMedGoogle Scholar
  14. Morin RD, O'Connor MD, Griffith M, Kuchenbauer F, Delaney A, Prabhu AL, Zhao Y, McDonald H, Zeng T, Hirst M, et al: Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res. 2008, 18: 610-621. 10.1101/gr.7179508.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Melton C, Judson RL, Blelloch R: Opposing microRNA families regulate self-renewal in mouse embryonic stem cells. Nature. 2010, 463: 621-626. 10.1038/nature08725.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Xu N, Papagiannakopoulos T, Pan G, Thomson JA, Kosik KS: MicroRNA-145 regulates OCT4, SOX2, and KLF4 and represses pluripotency in human embryonic stem cells. Cell. 2009, 137: 647-658. 10.1016/j.cell.2009.02.038.View ArticlePubMedGoogle Scholar
  17. Sengupta S, Nie J, Wagner RJ, Yang C, Stewart R, Thomson JA: MicroRNA 92b controls the G1/S checkpoint gene p57 in human embryonic stem cells. Stem cells. 2009, 27: 1524-1528. 10.1002/stem.84.View ArticlePubMedGoogle Scholar
  18. Lee NS, Kim JS, Cho WJ, Lee MR, Steiner R, Gompers A, Ling D, Zhang J, Strom P, Behlke M, et al: miR-302b maintains "stemness" of human embryonal carcinoma cells by post-transcriptional regulation of Cyclin D2 expression. Biochem Biophys Res Commun. 2008, 377: 434-440. 10.1016/j.bbrc.2008.09.159.View ArticlePubMedGoogle Scholar
  19. Zovoilis A, Smorag L, Pantazi A, Engel W: Members of the miR-290 cluster modulate in vitro differentiation of mouse embryonic stem cells. Differentiation. 2009, 78: 69-78. 10.1016/j.diff.2009.06.003.View ArticlePubMedGoogle Scholar
  20. Wang Y, Keys DN, Au-Young JK, Chen C: MicroRNAs in embryonic stem cells. J Cell Physiol. 2009, 218: 251-255. 10.1002/jcp.21607.View ArticlePubMedGoogle Scholar
  21. Allegrucci C, Young L: Differences between human embryonic stem cell lines. Human reproduction update. 2007, 13: 103-View ArticlePubMedGoogle Scholar
  22. Tsai ZY, Singh S, Yu SL, Kao LP, Chen BZ, Ho BC, Yang PC, Li SS: Identification of microRNAs regulated by activin A in human embryonic stem cells. J Cell Biochem. 2010, 109: 93-102.PubMedGoogle Scholar
  23. Thomson J, Parker J, Perou C, Hammond S: A custom microarray platform for analysis of microRNA gene expression. Nature Methods. 2004, 1: 47-53. 10.1038/nmeth704.View ArticlePubMedGoogle Scholar
  24. Shendure J, Ji H: Next-generation DNA sequencing. Nature biotechnology. 2008, 26: 1135-1145. 10.1038/nbt1486.View ArticlePubMedGoogle Scholar
  25. Li R, Yu C, Li Y, Lam T, Yiu S, Kristiansen K, Wang J: SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009, 25: 1966-10.1093/bioinformatics/btp336.View ArticlePubMedGoogle Scholar
  26. Winston K, Chen S, Betty T, Qian L, Lim K, Vivek T: Analysis of deep sequencing microRNA expression profile from human embryonic stem cells derived mesenchymal stem cells reveals possible role of let-7 microRNA family in downstream targeting of Hepatic Nuclear Factor 4 Alpha. BMC Genomics. 11:Google Scholar
  27. Fraser HB, Hirsh AE, Steinmetz LM, Scharfe C, Feldman MW: Evolutionary rate in the protein interaction network. Science. 2002, 296: 750-752. 10.1126/science.1068696.View ArticlePubMedGoogle Scholar
  28. Babiarz J, Ruby J, Wang Y, Bartel D, Blelloch R: Mouse ES cells express endogenous shRNAs, siRNAs, and other Microprocessor-independent, Dicer-dependent small RNAs. Genes dev. 2008, 22: 2773-10.1101/gad.1705308.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Marson A, Levine S, Cole M, Frampton G, Brambrink T, Johnstone S, Guenther M, Johnston W, Wernig M, Newman J: Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells. Cell. 2008, 134: 521-533. 10.1016/j.cell.2008.07.020.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Joglekar MV, Parekh VS, Mehta S, Bhonde RR, Hardikar AA: MicroRNA profiling of developing and regenerating pancreas reveal post-transcriptional regulation of neurogenin3. Dev Biol. 2007, 311: 603-612. 10.1016/j.ydbio.2007.09.008.View ArticlePubMedGoogle Scholar
  31. Goff LA, Davila J, Swerdel MR, Moore JC, Cohen RI, Wu H, Sun YE, Hart RP: Ago2 immunoprecipitation identifies predicted microRNAs in human embryonic stem cells and neural precursors. PLoS One. 2009, 4: e7192-10.1371/journal.pone.0007192.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Zhang R, Wang Y, Su B: Molecular evolution of a primate-specific microRNA family. Molecular biology and evolution. 2008, 25: 1493-10.1093/molbev/msn094.View ArticlePubMedGoogle Scholar
  33. Mineno J, Okamoto S, Ando T, Sato M, Chono H, Izu H, Takayama M, Asada K, Mirochnitchenko O, Inouye M: The expression profile of microRNAs in mouse embryos. Nucleic acids research. 2006, 34: 1765-10.1093/nar/gkl096.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Ahn H, Morin R, Zhao H, Harris R, Coarfa C, Chen Z, Milosavljevic A, Marra M, Rajkovic A: MicroRNA transcriptome in the newborn mouse ovaries determined by massive parallel sequencing. Molecular Human Reproduction. 2010, 16: 463-10.1093/molehr/gaq017.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Navara C, Mich-Basso J, Redinger C, Ben-Yehudah A, Jacoby E, Kovkarova-Naumovski E, Sukhwani M, Orwig K, Kaminski N, Castro C: Pedigreed primate embryonic stem cells express homogeneous familial gene profiles. Stem cells. 2007, 25: 2695-2704. 10.1634/stemcells.2007-0286.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Burdon T, Stracey C, Chambers I, Nichols J, Smith A: Suppression of SHP-2 and ERK signalling promotes self-renewal of mouse embryonic stem cells. Dev Biol. 1999, 210: 30-43. 10.1006/dbio.1999.9265.View ArticlePubMedGoogle Scholar
  37. Ying QL, Nichols J, Chambers I, Smith A: BMP induction of Id proteins suppresses differentiation and sustains embryonic stem cell self-renewal in collaboration with STAT3. Cell. 2003, 115: 281-292. 10.1016/S0092-8674(03)00847-X.View ArticlePubMedGoogle Scholar
  38. Pardal R, Molofsky A, He S, Morrison S: Stem cell self-renewal and cancer cell proliferation are regulated by common networks that balance the activation of proto-oncogenes and tumor suppressors. Cold Spring Harbor Laboratory Press. 2005, 177-Google Scholar
  39. Bar M, Wyman SK, Fritz BR, Qi J, Garg KS, Parkin RK, Kroh EM, Bendoraite A, Mitchell PS, Nelson AM, et al: MicroRNA discovery and profiling in human embryonic stem cells by deep sequencing of small RNA libraries. Stem cells. 2008, 26: 2496-2505. 10.1634/stemcells.2008-0356.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Liu L, Luo GZ, Yang W, Zhao X, Zheng Q, Lv Z, Li W, Wu HJ, Wang L, Wang XJ, Zhou Q: Activation of the imprinted Dlk1-Dio3 region correlates with pluripotency levels of mouse stem cells. J Biol Chem. 2010, 285: 19483-19490. 10.1074/jbc.M110.131995.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Hanna J, Cheng A, Saha K, Kim J, Lengner C, Soldner F, Cassady J, Muffat J, Carey B, Jaenisch R: Human embryonic stem cells with biological and epigenetic characteristics similar to those of mouse ESCs. Proc Natl Acad Sci USA. 2010, 107: 9222-10.1073/pnas.1004584107.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Lin J: Divergence measures based on the Shannon entropy. Information Theory, IEEE Transactions on. 1991, 37: 145-151. 10.1109/18.61115.View ArticleGoogle Scholar
  43. Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120: 15-20. 10.1016/j.cell.2004.12.035.View ArticlePubMedGoogle Scholar
  44. Rhead B, Karolchik D, Kuhn RM, Hinrichs AS, Zweig AS, Fujita PA, Diekhans M, Smith KE, Rosenbloom KR, Raney BJ: The UCSC Genome Browser database: update 2010. Nucleic Acids Res. 38: D613-619.Google Scholar
  45. Edgar RC: MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004, 5: 113-10.1186/1471-2105-5-113.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Huang da W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4: 44-57.View ArticlePubMedGoogle Scholar

Copyright

© Sun et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement